Roozbeh Kiani

This study shows macaques can plan ahead in new maze tasks without prior experience, avoiding deceptive paths based on future consequences.

This paper introduces REACH-Ctrl, a data-driven method for real-time control of cortical population activity using microstimulation and multi-electrode recordings, achieving high accuracy in macaque prefrontal cortex.

The paper investigates neural activity in the human basal ganglia-thalamic circuit during decision-making tasks, revealing dynamic representations and reward prediction error signals in motor thalamus.

This study shows how reward context is encoded through geometric shifts in neural representations, enabling rapid decision adjustments during learning.

The paper shows that TEa neurons encode decision variables as running averages without ramps during object-based decisions, contrasting with traditional models of evidence accumulation.

The paper introduces a method to design spatiotemporal stimulation patterns for neural circuits using dynamical systems and data-driven approaches to improve efficacy and selectivity.

This study uses recurrent neural networks to show how circuit compensation and re-learning mitigate inactivation effects, with varying impacts across tasks and network architectures.

The paper investigates differences in mnemonic accuracy for 'what', 'where', and 'when' components of recently-formed episodes and finds that 'when' is least accurate and most affected by memory load.

This paper introduces a neuron particle model to capture network topology and behavior from single units using statistical physics, showing improved predictions of animal behavior.

The paper shows that perceptual confidence and its deviations from accuracy can be explained by a Bayesian framework with incomplete knowledge of the environment.

The paper shows that subthalamic nucleus activity decreases during movement switches, challenging theories of movement suppression and revealing distinct neural coding for kinematics and action changes.

This study shows humans categorize faces by linearly integrating spatial and temporal evidence, adjusting feature weights for different category boundaries.

This study challenges conventional models of decision variable representation in the parietal cortex by showing opposite response patterns in a face-discrimination task compared to motion-discrimination tasks.

The paper decodes real-time decision variables from motor cortex activity and shows their link to choice behavior and decision dynamics.

The paper develops a framework to predict the effects of neural perturbations using functional causal flow inferred from resting state activity in monkeys.

This paper introduces a probabilistic neural model inspired by sparse random connectivity that efficiently estimates spiking patterns in neural circuits and outperforms existing models.

The paper analyzes spiking patterns in monkey prefrontal cortex during a visual task, showing strong correlations in encoding but simple decoding.

This study demonstrates evidence integration at prolonged timescales using a new psychophysical task and model-based analyses of human behavior.

This study shows that psychophysical reverse correlation reflects both sensory and decision-making processes and can be distorted by decision-related factors.

This study investigates whether the brain represents higher-order statistical moments of dynamic stimuli and finds that humans are insensitive to skewness and kurtosis in coherent random dot motion.

This study examines how decision variables are represented in neural populations of PMd and M1 during a motor task, showing differences in timing and strength between areas and replicating findings with a computational model.